To further evaluate the generality, we validate the proposed method on an office-home dataset and compare the performance with 19 SOTA methods as reported in Tables REF  and REF , including adversarial-based methods, difference-based methods, and mixed methods. It can be seen that the four domains in Office-Home form 12 different UDA tasks, and the proposed TVT+SAFF achieves the best performance on all tasks, with an average accuracy of 84.86\(\%\) , which outperforms other UDA methods significantly.
As shown in Table REF , DANN [1]} consistently underperforms, and other adversarial-based methods such as TAT [2]}, CDAN+E [3]} and CVB-GD [4]} perform slightly better, but still unsatisfactory. JAN [5]} is one of the early attempts of difference-based methods, which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Similarly, TransDA [6]}, TPN [7]}, SHOT [8]}, DeiT-S [9]}, Liu et al [10]}, GVB-GD+SPCL [11]}, ATDOC-NA [12]}, SFDA-DE [13]}, and WinTR-S [14]} utilize strategies such as optimal transport theory, pseudo-labels, etc. to align domain features. Although these methods achieve better performance than adversarial-based methods, they ignore styles between different domain features. In contrast, our proposed SAFF can make full use of multiple-style information to enrich the latent feature space and greatly improves the classification accuracy.
